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Synopsis

Computers are traditionally viewed as logical machines which follow precise, deterministic instructions. The real world in which they operate, however, is full of complexity, ambiguity, and uncertainty.

In this year’s Turing Lecture, Professor Chris Bishop discusses the field of machine learning, and shows how uncertainty can be modelled and quantified using probabilities.

He looks at the recent developments in probabilistic modelling which have greatly expanded the variety and scale of machine learning applications, and he explores the future potential for this technology.